The rise of artificial intelligence will have huge economic implications, disrupting every industry and every member of the workforce. It will create new jobs (2.3 million by 2020, according to research company Gartner) and countless opportunities, but it also has the potential to widen the divide between the haves and have-nots. How can we embrace this technology while creating inclusive opportunities for everybody in the age of Man Machine? The answer is simple: everyone needs to step up. Combating growing social, economic, and financial disparities will require every individual, business, educational institution and government to play a role in preparing the global workforce to thrive alongside intelligent machines.

Every year I give my forecast for the top 10 workplace trends for the upcoming year. The purpose is to help prepare organizations for the future by collecting, assessing and reporting the trends that will most impact them. You can read my predictions from 2013, 2014, 2015, 2016 and 2017. These trends are based on hundreds of conversations with executives and workers, a series of national and global online surveys and secondary research from more than 450 different research sources, including colleges, consulting firms, non-profits, the government and trade associations. All economic indicators show a positive view of the U.S. economy in 2018.

If you want to learn how to perform real advanced statistical analyses in the R program, you have come to the right place. Now you don't have to scour the web endlessly in order to find how to do an analysis of covariance or a mixed analysis of variance, how to execute a binomial logistic regression, how to perform a multidimensional scaling or a factor analysis. Everything is here, in this course, explained visually, step by step. So, what's covered in this course? First of all, we are going to study some more techniques to evaluate the mean differences.

A lot of people are trying to make sense of artificial intelligence (AI) these days--what it can do for their business, and more specifically, their big data. Count Thad Lurie, CAE, among them. Last week, he made a career move--from chief information officer and vice president of operations at EDUCAUSE, a higher education technology association, to vice president of business intelligence and performance at Experient, an event management company that is doing a lot of work around event technology and attendees' behavioral data. The job change coincides with a significant mindset shift for Lurie that has him thinking about the power of artificial intelligence, not just as a tool to augment existing products or services but as a way to completely reimagine the membership experience. "We need to look at our business models through the lens of AI and in a completely different way because there may be member services and programs that we can do differently," Lurie says.

Several use cases showcasing different solutions are covered in this video. Introduction to Amazon SageMaker (10 minutes) This course provides an overview of Amazon SageMaker, a fully managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models. Introduction to AWS Greengrass (10 minutes) This course is an introduction to AWS Greengrass, which lets you run local compute, messaging, data caching, and sync capabilities for connected devices in a secure way. You can build machine learning models in the cloud and execute their inference at the edge. Introduction to Amazon Comprehend (10 minutes) This course introduces you to Amazon Comprehend, a new AWS service that helps with natural language processing. In this course, we discuss how Amazon Comprehend solves challenges like the exponential growth of unstructured text, explore the service's five main capabilities, and review some popular use cases.

Machine Learning or ML is fast becoming the buzzword of our time, but why are so many developers falling short when it comes to getting their heads round this essential skill? Here's why developers must tackle ML to get ahead and what's standing in their way. Let's face it, when it comes to AI, the future has very much arrived. This application of ML is everywhere right now, whether you're looking at self-driving cars or self-tuning database systems – it's impacting almost every industry on the market. Acquiring ML skills is a no-brainer for the ambitious developer, and the number of self-led courses and MOOCs doubled last year.

In each century, education has found a way to reach the masses. In the 21st century, the answer lies in artificial education. For years, there have been murmurs about robots molding the next generation and taking over every aspect of teaching and learning. With children and adults increasingly using tablets and coding becoming part of national curricula around the world, technology is becoming an integral part of classrooms, just like chalk and blackboards. More importantly, it will allow learners to strengthen their skills and education as education and artificial intelligence begin to have a partnership that will grow in the years to come.

We will begin this course with a tutorial on the standard camera models used in computer vision. These models allow us to understand, in a geometric fashion, how light from a scene enters a camera and projects onto a 2D image. By defining these models mathematically, we will be able understand exactly how a point in 3D corresponds to a point in the image and how an image will change as we move a camera in a 3D environment. In the later modules, we will be able to use this information to perform complex perception tasks such as reconstructing 3D scenes from video.

Knowledge of Geographic Information Systems (GIS) is an increasingly sought after skill in industries from agriculture to public health. This Specialization, offered in partnership with ArcGIS developer Esri, will teach the skills you need to successfully use GIS software in a professional setting. You will learn how to analyze your spatial data, use cartography techniques to communicate your results in maps, and collaborate with peers in GIS and GIS-dependent fields. In the final Capstone Project, you will create a professional-quality GIS portfolio piece using a combination of data identification and collection, analytical map development, and spatial analysis techniques.

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!